استفاده از الگوریتم های اجماع برای بهینه سازی معماری میکروسرویس
محورهای موضوعی : پردازش چند رسانه ای، سیستمهای ارتباطی، سیستمهای هوشمنداسماعیل صادقی هفشجانی 1 , محمود دی پیر 2 , علی برومندنیا 3
1 - گروه مهندسی کامپیوتر، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران
2 - گروه مهندسی کامپیوتر، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران
3 - گروه مهندسی کامپیوتر، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران
کلید واژه: الگوریتم‌های اجماع, پروتکلPaxos , تحمل خطا, تخصیص منابع, میکروسرویس, مقیاس‌پذیری,
چکیده مقاله :
معماری‌های میکروسرویس (MSAs) به یک رویکرد اساسی برای ایجاد سیستمهای توزیع‌شده مقیاس‌پذیر تبدیل شده‌اند. با این حال، دستیابی به سازگاری و تحمل خطا همچنان یک چالش بزرگ است. این مطالعه با هدف بهبود عملکرد و قابلیت اطمینان سیستمهای میکروسرویس با ادغام الگوریتم‌های اجماع Paxos انجام شده است. روش‌شناسی ما شامل استفاده از Paxos برای تضمین سازگاری و تحمل خطا در سیستمهای میکروسرویس می‌باشد. شبیه‌سازی‌ها و مطالعات موردی برای ارزیابی عملکرد رویکرد ما انجام شده است. نتایج نشان می‌دهد که Paxos تأخیر را کاهش داده، رقابت منابع را به حداقل می‌رساند و سازگاری حالت را در خدمات توزیع‌شده تضمین می‌کند. بهینه‌سازی مبتنی بر Paxos نه تنها عملکرد را بهبود می‌بخشد، بلکه قابلیت اطمینان و دسترسی سیستمهای میکروسرویس را نیز افزایش می‌دهد. این مطالعه مزایای قابل توجهی از Paxos در بهینه‌سازی استقرارهای میکروسرویس را نشان می‌دهد.
Introduction: Microservice architectures (MSAs) have transformed the development of scalable distributed systems by breaking monolithic applications into smaller, independent services. Despite their advantages in scalability, flexibility, and maintenance, MSAs face significant challenges in achieving state consistency and fault tolerance. This study aims to address these issues by leveraging Paxos consensus algorithms to enhance the reliability and performance of microservice systems.
Method: The proposed approach integrates Paxos consensus mechanisms into microservice systems to ensure state consistency and fault tolerance. Simulations and real-world case studies were conducted, comparing our methodology with existing optimization frameworks, such as SPNs and NSGA-II. This method emphasizes dynamic resource allocation and automated scaling under various workload conditions.
Results: The results demonstrate that Paxos-based optimization reduces latency by 25%, increases throughput by 30%, and enhances resource utilization efficiency by 20%. Additionally, it achieves a high consistency rate of 99.9%, ensuring reliable state updates across distributed nodes. The system also showed robust fault tolerance, maintaining 95% operational success during node failures.
Discussion: The findings highlight the superiority of Paxos in optimizing microservice deployments, particularly in dynamic cloud environments. This study underscores the importance of integrating consensus protocols into optimization frameworks for improved performance, reliability, and scalability. Future research could explore further enhancements in energy efficiency, security measures, and deployment across diverse cloud scenarios.
[1] J. Esparza-Peidro, F. D. Muñoz-Escoí, and J. M. Bernabéu-Aubán, “Modeling microservice architectures,” J. Syst. Softw., vol. 213, p. 112041, Jul. 2024, doi: 10.1016/J.JSS.2024.112041.
[2] “Modeling microservice architectures - ScienceDirect.” Accessed: Jul. 27, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S0164121224000840
[3] J. Esparza-Peidro, F. D. Muñoz-Escoí, and J. M. Bernabéu-Aubán, “Modeling microservice architectures,” J. Syst. Softw., vol. 213, Jul. 2024, doi: 10.1016/j.jss.2024.112041.
[4] M. Diogo, B. Cabral, and J. Bernardino, “Consistency models of NoSQL databases,” Futur. Internet, vol. 11, no. 2, 2019, doi: 10.3390/FI11020043.
[5] X. Zhou et al., “Revisiting the practices and pains of microservice architecture in reality: An industrial inquiry,” J. Syst. Softw., vol. 195, Jan. 2023, doi: 10.1016/j.jss.2022.111521.
[6] J. Zhao, Y. Zhang, J. Jiang, Z. Hua, and Y. Xiang, “A secure dynamic cross-chain decentralized data consistency verification model,” J. King Saud Univ. - Comput. Inf. Sci., vol. 36, no. 1, p. 101897, 2024, doi: 10.1016/j.jksuci.2023.101897.
[7] Y. Song, P. Dhariwal, M. Chen, and I. Sutskever, “Consistency Models,” Proc. Mach. Learn. Res., vol. 202, pp. 32211–32252, 2023, doi: 10.1007/978-1-4842-1329-2_9.
[8] P. Bailis, S. Venkataraman, M. J. Franklin, J. M. Hellerstein, and I. Stoica, “Quantifying eventual consistency with PBS,” VLDB J., vol. 23, no. 2, pp. 279–302, 2014, doi: 10.1007/S00778-013-0330-1.
[9] M. ul Hassan et al., “An efficient dynamic decision-based task optimization and scheduling approach for microservice-based cost management in mobile cloud computing applications,” Pervasive Mob. Comput., vol. 92, p. 101785, 2023, doi: https://doi.org/10.1016/j.pmcj.2023.101785.
[10] L. Lelovic et al., “Change impact analysis in microservice systems: A systematic literature review,” J. Syst. Softw., vol. 219, p. 112241, 2025, doi: https://doi.org/10.1016/j.jss.2024.112241.
[11] M. Baboi, A. Iftene, and D. Gîfu, “Dynamic Microservices to Create Scalable and Fault Tolerance Architecture,” Procedia Comput. Sci., vol. 159, pp. 1035–1044, Jan. 2019, doi: 10.1016/J.PROCS.2019.09.271.
[12] M. Baboi, A. Iftene, and D. Gîfu, “Dynamic Microservices to Create Scalable and Fault Tolerance Architecture,” Procedia Comput. Sci., vol. 159, pp. 1035–1044, 2019, doi: 10.1016/j.procs.2019.09.271.
[13] N. Bansal, A. Awasthi, and S. Bansal, “Task scheduling algorithms with multiple factor in cloud computing environment,” Advances in Intelligent Systems and Computing, vol. 433. pp. 619–627, 2016. doi: 10.1007/978-81-322-2755-7_64.
[14] A. K. Jain, N. Gupta, and B. B. Gupta, “A survey on scalable consensus algorithms for blockchain technology,” Cyber Secur. Appl., vol. 3, p. 100065, Dec. 2025, doi: 10.1016/J.CSA.2024.100065.
[15] T. F. da Silva Pinheiro, P. Pereira, B. Silva, and P. Maciel, “A performance modeling framework for microservices-based cloud infrastructures,” J. Supercomput., vol. 79, no. 7, pp. 7762–7803, May 2023, doi: 10.1007/S11227-022-04967-6/METRICS.
[16] A. Zappone and E. A. Jorswieck, “Energy-efficient resource allocation in future wireless networks by sequential fractional programming,” Digit. Signal Process., vol. 60, pp. 324–337, Jan. 2017, doi: 10.1016/J.DSP.2016.09.014.
[17] H. Guo, H. Cao, J. He, X. Liu, and Y. Shi, “POBO: Safe and optimal resource management for cloud microservices,” Perform. Eval., vol. 162, p. 102376, Nov. 2023, doi: 10.1016/J.PEVA.2023.102376.
[18] B. Cai, B. Wang, M. Yang, and Q. Guo, “AutoMan: Resource-efficient provisioning with tail latency guarantees for microservices,” Futur. Gener. Comput. Syst., vol. 143, pp. 61–75, Jun. 2023, doi: 10.1016/J.FUTURE.2023.01.014.
[19] V. Ramamoorthi, “AI-Enhanced Performance Optimization for Microservice-Based Systems,” vol. 4, no. September, pp. 1–7, 2024, doi: 10.69987/JACS.2024.40901.
[20] B. Barua and M. S. Kaiser, “Leveraging Microservices Architecture for Dynamic Pricing in the Travel Industry : Algorithms , Scalability , and Impact on Revenue and Customer Satisfaction,” pp. 1–19.
[21] X. Zhou et al., “Revisiting the practices and pains of microservice architecture in reality: An industrial inquiry,” J. Syst. Softw., vol. 195, p. 111521, Jan. 2023, doi: 10.1016/J.JSS.2022.111521.
[22] A. Munir, “Design and Implementation of a Smart Peer-to-Peer Energy Exchange Platform,” IEEE Trans. Smart Grid, vol. 8, no. 4, pp. 1730–1741, 2017.
